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1
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Chen ZZ, Cheng N, Johnson L, Dufresne J, Marshall JG. Ammonium bicarbonate buffer system for DNA hybridization and quantification by LC-ESI-MS/MS. Anal Biochem 2025; 702:115822. [PMID: 40054549 DOI: 10.1016/j.ab.2025.115822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2024] [Revised: 02/09/2025] [Accepted: 02/23/2025] [Indexed: 03/18/2025]
Abstract
High salt buffer may be used for the UV/VIS or radiometric based detection of trihybrid DNA but would contaminate the electrospray mass spectrometer. Colorimetric DNA hybridization assays with the substrate BCIP/NBT reacted with the alkaline phosphatase-streptavidin (APSA) enzyme conjugate that showed a linear range for HIV DNA from 1 pM to 100 pM DNA in presence of high salt concentrations. Ammonia bicarbonate (AMBIC) or ethanolamine resulted in strong DNA hybridization similar to NaCl but was compatible with specific and sensitive mass spectrometry. Linear and Gaussian analysis of HIV DNA with 10 % error was achieved across the pico Molar range from APSA amplification that converted the substrate AMP to adenosine for detection by monitoring the precursor ion at m/z 268 and plotting the fragment intensity at m/z 136 (m/z 268→ m/z 136) that was linear to 100 fM after log transformation. The novel observation that specific DNA hybridization in NaCl may be substituted with AMBIC permitted the direct analysis of a target DNA in femto molar to pico molar range by enzyme linked mass spectrometric assay (ELiMSA) using as little as 0.1 μL (100 nL) of sample reaction injected on column.
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Affiliation(s)
- Zhuo Zhen Chen
- Research Analytical Biochemistry Laboratory, Department of Chemistry and Biology, Ryerson University, Canada
| | - Nan Cheng
- Research Analytical Biochemistry Laboratory, Department of Chemistry and Biology, Ryerson University, Canada
| | - Lloyd Johnson
- Research Analytical Biochemistry Laboratory, Department of Chemistry and Biology, Ryerson University, Canada
| | - Jaimie Dufresne
- Research Analytical Biochemistry Laboratory, Department of Chemistry and Biology, Ryerson University, Canada
| | - John G Marshall
- Research Analytical Biochemistry Laboratory, Department of Chemistry and Biology, Ryerson University, Canada.
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2
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Kim YK, Ramalho-Santos M. 20 years of stemness: From stem cells to hypertranscription and back. Stem Cell Reports 2025; 20:102406. [PMID: 39919752 PMCID: PMC11960510 DOI: 10.1016/j.stemcr.2025.102406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2024] [Revised: 01/08/2025] [Accepted: 01/09/2025] [Indexed: 02/09/2025] Open
Abstract
Transcriptional profiling of stem cells came of age at the beginning of the century with the use of microarrays to analyze cell populations in bulk. Since then, stem cell transcriptomics has become increasingly sophisticated, notably with the recent widespread use of single-cell RNA sequencing. Here, we provide a perspective on how an early signature of genes upregulated in embryonic and adult stem cells, identified using microarrays over 20 years ago, serendipitously led to the recent discovery that stem/progenitor cells across organs are in a state of hypertranscription, a global elevation of the transcriptome. Looking back, we find that the 2002 stemness signature is a robust marker of stem cell hypertranscription, even though it was developed well before it was known what hypertranscription meant or how to detect it. We anticipate that studies of stem cell hypertranscription will be rich in novel insights in physiological and disease contexts for years to come.
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Affiliation(s)
- Yun-Kyo Kim
- Program in Developmental and Stem Cell Biology, Hospital for Sick Children, Toronto ON M5G 0A4, Canada; Department of Molecular Genetics, University of Toronto, Toronto ON M5G 1X5, Canada.
| | - Miguel Ramalho-Santos
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto ON M5T 3L9, Canada; Department of Molecular Genetics, University of Toronto, Toronto ON M5G 1X5, Canada.
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3
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Cruz-Ramírez LA, Cedillo-Jiménez C, Albarrán-Tamayo F, Bañuelos-Hernández B, Morales-Alonso SI, Cruz-Hernández A. MicroRNA Profiling in Non-model Plants Using Microarray Hybridization. Methods Mol Biol 2025; 2900:145-159. [PMID: 40380059 DOI: 10.1007/978-1-0716-4398-3_9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/19/2025]
Abstract
The aim of this chapter is to describe how microarrays can be used to identify miRNAs that participate in fruit development in a non-model plant: prickly pear (Opuntia spp). Here, we describe a technology including molecular biology and bioinformatics methods combined to identify, select, and isolate candidate miRNAs putatively involved in prickly pear development. We describe how miRNA expression analysis through microarray hybridization was performed and the expression of selected miRNAs validated using RT-PCR.
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Affiliation(s)
- Luis Alfredo Cruz-Ramírez
- Molecular and Developmental Complexity Group, Unit for Advanced Genomics, Cinvestav, Irapuato, Guanajuato, Mexico
| | | | | | | | | | - Andrés Cruz-Hernández
- Biological Sciences Laboratory, Universidad Lasalle Bajío, León, Guanajuato, Mexico.
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4
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Kutchy NA, Morenikeji OB, Memili A, Ugur MR. Deciphering sperm functions using biological networks. Biotechnol Genet Eng Rev 2024; 40:3743-3767. [PMID: 36722689 DOI: 10.1080/02648725.2023.2168912] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2022] [Indexed: 02/02/2023]
Abstract
The global human population is exponentially increasing, which requires the production of quality food through efficient reproduction as well as sustainable production of livestock. Lack of knowledge and technology for assessing semen quality and predicting bull fertility is hindering advances in animal science and food animal production and causing millions of dollars of economic losses annually. The intent of this systemic review is to summarize methods from computational biology for analysis of gene, metabolite, and protein networks to identify potential markers that can be applied to improve livestock reproduction, with a focus on bull fertility. We provide examples of available gene, metabolic, and protein networks and computational biology methods to show how the interactions between genes, proteins, and metabolites together drive the complex process of spermatogenesis and regulate fertility in animals. We demonstrate the use of the National Center for Biotechnology Information (NCBI) and Ensembl for finding gene sequences, and then use them to create and understand gene, protein and metabolite networks for sperm associated factors to elucidate global cellular processes in sperm. This study highlights the value of mapping complex biological pathways among livestock and potential for conducting studies on promoting livestock improvement for global food security.
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Affiliation(s)
- Naseer A Kutchy
- Department of Anatomy, Physiology and Pharmacology, School of Veterinary Medicine, St. George's University, St. George's, Grenada
- Department of Animal Sciences, School of Environmental and Biological Sciences Rutgers, The State University of New Jersey, New Brunswick, NJ, USA
| | - Olanrewaju B Morenikeji
- Division of Biological and Health Sciences, University of Pittsburgh at Bradford, Bradford, PA, USA
| | - Aylin Memili
- Department of Nutrition, Gillings School of Global Public Health, University of North Carolina-Chapel Hill, Chapel Hill, NC, USA
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5
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Nur A, Lai JY, Ch'ng ACW, Choong YS, Wan Isa WYH, Lim TS. A review of in vitro stochastic and non-stochastic affinity maturation strategies for phage display derived monoclonal antibodies. Int J Biol Macromol 2024; 277:134217. [PMID: 39069045 DOI: 10.1016/j.ijbiomac.2024.134217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2024] [Revised: 07/24/2024] [Accepted: 07/25/2024] [Indexed: 07/30/2024]
Abstract
Monoclonal antibodies identified using display technologies like phage display occasionally suffers from a lack of affinity making it unsuitable for application. This drawback is circumvented with the application of affinity maturation. Affinity maturation is an essential step in the natural evolution of antibodies in the immune system. The evolution of molecular based methods has seen the development of various mutagenesis approaches. This allows for the natural evolutionary process during somatic hypermutation to be replicated in the laboratories for affinity maturation to fine-tune the affinity and selectivity of antibodies. In this review, we will discuss affinity maturation strategies for mAbs generated through phage display systems. The review will highlight various in vitro stochastic and non-stochastic affinity maturation approaches that includes but are not limited to random mutagenesis, site-directed mutagenesis, and gene synthesis.
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Affiliation(s)
- Alia Nur
- Institute for Research in Molecular Medicine, Universiti Sains Malaysia, 11800 Penang, Malaysia
| | - Jing Yi Lai
- Institute for Research in Molecular Medicine, Universiti Sains Malaysia, 11800 Penang, Malaysia
| | - Angela Chiew Wen Ch'ng
- Institute for Research in Molecular Medicine, Universiti Sains Malaysia, 11800 Penang, Malaysia
| | - Yee Siew Choong
- Institute for Research in Molecular Medicine, Universiti Sains Malaysia, 11800 Penang, Malaysia
| | - Wan Yus Haniff Wan Isa
- School of Medical Sciences, Department of Medicine, Universiti Sains Malaysia, 16150 Kubang Kerian, Kelantan, Malaysia
| | - Theam Soon Lim
- Institute for Research in Molecular Medicine, Universiti Sains Malaysia, 11800 Penang, Malaysia; Analytical Biochemistry Research Centre, Universiti Sains Malaysia, 11800 Penang, Malaysia.
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6
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Zhu G, Wang X, Wang Y, Huang T, Zhang X, He J, Shi N, Chen J, Zhang J, Zhang M, Li J. Comparative transcriptomic study on the ovarian cancer between chicken and human. Poult Sci 2024; 103:104021. [PMID: 39002367 PMCID: PMC11298922 DOI: 10.1016/j.psj.2024.104021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Revised: 06/05/2024] [Accepted: 06/19/2024] [Indexed: 07/15/2024] Open
Abstract
The laying hen is the spontaneous model of ovarian tumor. A comprehensive comparison based on RNA-seq from hens and women may shed light on the molecular mechanisms of ovarian cancer. We performed next-generation sequencing of microRNA and mRNA expression profiles in 9 chicken ovarian cancers and 4 normal ovaries, which has been deposited in GSE246604. Together with 6 public datasets (GSE21706, GSE40376, GSE18520, GSE27651, GSE66957, TCGA-OV), we conducted a comparative transcriptomics study between chicken and human. In the present study, miR-451, miR-2188-5p, and miR-10b-5p were differentially expressed in normal ovaries, early- and late-stage ovarian cancers. We also disclosed 499 up-regulated genes and 1,061 down-regulated genes in chicken ovarian cancer. The molecular signals from 9 cancer hallmarks, 25 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and 369 Gene Ontology (GO) pathways exhibited abnormalities in ovarian cancer compared to normal ovaries via Gene Set Enrichment Analysis (GSEA). In the comparative analysis across species, we have uncovered the conservation of 5 KEGG and 76 GO pathways between chicken and human including the mismatch repair and ECM receptor interaction pathways. Moreover, a total of 174 genes contributed to the core enrichment for these KEGG and GO pathways were identified. Among these genes, the 22 genes were found to be associated with overall survival in patients with ovarian cancer. In general, we revealed the microRNA profiles of ovarian cancers in hens and updated the mRNA profiles previously derived from microarrays. And we also disclosed the molecular pathways and core genes of ovarian cancer shared between hens and women, which informs model animal studies and gene-targeted drug development.
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Affiliation(s)
- Guoqiang Zhu
- Key laboratory of Bio-resources and Eco-environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu 610065, China; Animal Disease Prevention and Food Safety Key Laboratory of Sichuan Province, College of Life Sciences, Sichuan University, Chengdu, China
| | - Xinglong Wang
- Key laboratory of Bio-resources and Eco-environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu 610065, China; Animal Disease Prevention and Food Safety Key Laboratory of Sichuan Province, College of Life Sciences, Sichuan University, Chengdu, China
| | - Yajun Wang
- Key laboratory of Bio-resources and Eco-environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu 610065, China; Animal Disease Prevention and Food Safety Key Laboratory of Sichuan Province, College of Life Sciences, Sichuan University, Chengdu, China
| | - Tianjiao Huang
- Key laboratory of Bio-resources and Eco-environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu 610065, China; Animal Disease Prevention and Food Safety Key Laboratory of Sichuan Province, College of Life Sciences, Sichuan University, Chengdu, China
| | - Xiao Zhang
- Key laboratory of Bio-resources and Eco-environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu 610065, China; Animal Disease Prevention and Food Safety Key Laboratory of Sichuan Province, College of Life Sciences, Sichuan University, Chengdu, China
| | - Jiliang He
- Key laboratory of Bio-resources and Eco-environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu 610065, China; Animal Disease Prevention and Food Safety Key Laboratory of Sichuan Province, College of Life Sciences, Sichuan University, Chengdu, China
| | - Ningkun Shi
- Key laboratory of Bio-resources and Eco-environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu 610065, China; Animal Disease Prevention and Food Safety Key Laboratory of Sichuan Province, College of Life Sciences, Sichuan University, Chengdu, China
| | - Juntao Chen
- Key laboratory of Bio-resources and Eco-environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu 610065, China; Animal Disease Prevention and Food Safety Key Laboratory of Sichuan Province, College of Life Sciences, Sichuan University, Chengdu, China
| | - Jiannan Zhang
- Key laboratory of Bio-resources and Eco-environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu 610065, China; Animal Disease Prevention and Food Safety Key Laboratory of Sichuan Province, College of Life Sciences, Sichuan University, Chengdu, China
| | - Mao Zhang
- Division of Vascular Surgery, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 610072, China
| | - Juan Li
- Key laboratory of Bio-resources and Eco-environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu 610065, China; Animal Disease Prevention and Food Safety Key Laboratory of Sichuan Province, College of Life Sciences, Sichuan University, Chengdu, China.
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7
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Gao Z, Lu Y, Li M, Chong Y, Hong J, Wu J, Wu D, Xi D, Deng W. Application of Pan-Omics Technologies in Research on Important Economic Traits for Ruminants. Int J Mol Sci 2024; 25:9271. [PMID: 39273219 PMCID: PMC11394796 DOI: 10.3390/ijms25179271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2024] [Revised: 08/23/2024] [Accepted: 08/26/2024] [Indexed: 09/15/2024] Open
Abstract
The economic significance of ruminants in agriculture underscores the need for advanced research methodologies to enhance their traits. This review aims to elucidate the transformative role of pan-omics technologies in ruminant research, focusing on their application in uncovering the genetic mechanisms underlying complex traits such as growth, reproduction, production performance, and rumen function. Pan-omics analysis not only helps in identifying key genes and their regulatory networks associated with important economic traits but also reveals the impact of environmental factors on trait expression. By integrating genomics, epigenomics, transcriptomics, metabolomics, and microbiomics, pan-omics enables a comprehensive analysis of the interplay between genetics and environmental factors, offering a holistic understanding of trait expression. We explore specific examples of economic traits where these technologies have been pivotal, highlighting key genes and regulatory networks identified through pan-omics approaches. Additionally, we trace the historical evolution of each omics field, detailing their progression from foundational discoveries to high-throughput platforms. This review provides a critical synthesis of recent advancements, offering new insights and practical recommendations for the application of pan-omics in the ruminant industry. The broader implications for modern animal husbandry are discussed, emphasizing the potential for these technologies to drive sustainable improvements in ruminant production systems.
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Affiliation(s)
- Zhendong Gao
- Yunnan Provincial Key Laboratory of Animal Nutrition and Feed, Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China
- State Key Laboratory for Conservation and Utilization of Bio-Resource in Yunnan, Kunming 650201, China
| | - Ying Lu
- Yunnan Provincial Key Laboratory of Animal Nutrition and Feed, Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China
| | - Mengfei Li
- Yunnan Provincial Key Laboratory of Animal Nutrition and Feed, Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China
| | - Yuqing Chong
- Yunnan Provincial Key Laboratory of Animal Nutrition and Feed, Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China
| | - Jieyun Hong
- Yunnan Provincial Key Laboratory of Animal Nutrition and Feed, Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China
| | - Jiao Wu
- Yunnan Provincial Key Laboratory of Animal Nutrition and Feed, Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China
| | - Dongwang Wu
- Yunnan Provincial Key Laboratory of Animal Nutrition and Feed, Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China
| | - Dongmei Xi
- Yunnan Provincial Key Laboratory of Animal Nutrition and Feed, Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China
| | - Weidong Deng
- Yunnan Provincial Key Laboratory of Animal Nutrition and Feed, Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China
- State Key Laboratory for Conservation and Utilization of Bio-Resource in Yunnan, Kunming 650201, China
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8
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Gong X, Su L, Huang J, Liu J, Wang Q, Luo X, Yang G, Chi H. An overview of multi-omics technologies in rheumatoid arthritis: applications in biomarker and pathway discovery. Front Immunol 2024; 15:1381272. [PMID: 39139555 PMCID: PMC11319186 DOI: 10.3389/fimmu.2024.1381272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2024] [Accepted: 07/12/2024] [Indexed: 08/15/2024] Open
Abstract
Rheumatoid arthritis (RA) is a chronic inflammatory autoimmune disease with a complex pathological mechanism involving autoimmune response, local inflammation and bone destruction. Metabolic pathways play an important role in immune-related diseases and their immune responses. The pathogenesis of rheumatoid arthritis may be related to its metabolic dysregulation. Moreover, histological techniques, including genomics, transcriptomics, proteomics and metabolomics, provide powerful tools for comprehensive analysis of molecular changes in biological systems. The present study explores the molecular and metabolic mechanisms of RA, emphasizing the central role of metabolic dysregulation in the RA disease process and highlighting the complexity of metabolic pathways, particularly metabolic remodeling in synovial tissues and its association with cytokine-mediated inflammation. This paper reveals the potential of histological techniques in identifying metabolically relevant therapeutic targets in RA; specifically, we summarize the genetic basis of RA and the dysregulated metabolic pathways, and explore their functional significance in the context of immune cell activation and differentiation. This study demonstrates the critical role of histological techniques in decoding the complex metabolic network of RA and discusses the integration of histological data with other types of biological data.
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Affiliation(s)
- Xiangjin Gong
- Department of Sports Rehabilitation, Southwest Medical University, Luzhou, China
| | - Lanqian Su
- Clinical Medical College, Southwest Medical University, Luzhou, China
| | - Jinbang Huang
- Clinical Medical College, Southwest Medical University, Luzhou, China
| | - Jie Liu
- Department of Geriatric, Dazhou Central Hospital, Dazhou, China
| | - Qinglai Wang
- Orthopedics and Traumatology Department of TCM, Wenzhou TCM Hospital of Zhejiang Chinese Medical University, Wenzhou, China
| | - Xiufang Luo
- Department of Geriatric, Dazhou Central Hospital, Dazhou, China
| | - Guanhu Yang
- Department of Specialty Medicine, Ohio University, Athens, OH, United States
| | - Hao Chi
- Clinical Medical College, Southwest Medical University, Luzhou, China
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9
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Aghaieabiane N, Koutis I. SGCP: a spectral self-learning method for clustering genes in co-expression networks. BMC Bioinformatics 2024; 25:230. [PMID: 38956463 PMCID: PMC11221046 DOI: 10.1186/s12859-024-05848-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 06/18/2024] [Indexed: 07/04/2024] Open
Abstract
BACKGROUND A widely used approach for extracting information from gene expression data employs the construction of a gene co-expression network and the subsequent computational detection of gene clusters, called modules. WGCNA and related methods are the de facto standard for module detection. The purpose of this work is to investigate the applicability of more sophisticated algorithms toward the design of an alternative method with enhanced potential for extracting biologically meaningful modules. RESULTS We present self-learning gene clustering pipeline (SGCP), a spectral method for detecting modules in gene co-expression networks. SGCP incorporates multiple features that differentiate it from previous work, including a novel step that leverages gene ontology (GO) information in a self-leaning step. Compared with widely used existing frameworks on 12 real gene expression datasets, we show that SGCP yields modules with higher GO enrichment. Moreover, SGCP assigns highest statistical importance to GO terms that are mostly different from those reported by the baselines. CONCLUSION Existing frameworks for discovering clusters of genes in gene co-expression networks are based on relatively simple algorithmic components. SGCP relies on newer algorithmic techniques that enable the computation of highly enriched modules with distinctive characteristics, thus contributing a novel alternative tool for gene co-expression analysis.
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Affiliation(s)
- Niloofar Aghaieabiane
- Computer Science Department, New Jersey Institute of Technology, Newark, NJ, 07102, USA
| | - Ioannis Koutis
- Computer Science Department, New Jersey Institute of Technology, Newark, NJ, 07102, USA.
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10
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Ünal İ, Cansız D, Beler M, Alturfan AA, Emekli-Alturfan E. Whole-Mount RNA In Situ Hybridization of Zebrafish Embryos. Methods Mol Biol 2024; 2753:543-551. [PMID: 38285366 DOI: 10.1007/978-1-0716-3625-1_35] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2024]
Abstract
A commonly employed technique in molecular biology to evaluate the temporal and spatial expression of a certain gene is in situ hybridization. This method is an effective strategy to construct synexpression groups, co-expressed genes acting in shared biological processes, and to find new members of genes engaged in the same signaling pathways to discover similar spatial and temporal expression patterns in zebrafish embryos. The major disadvantage of this method is that RNA probes can penetrate within 2 days of post-fertilization embryos, and therefore, in later developmental stages, the probe can only reach the surface tissues. Further application of the method in histological sections will be required for a complete and accurate gene expression investigation. However, this method is highly effective at late embryogenesis and early larval stages for observing gene expression in endodermal derivatives and sensory organs. RNA probes for in situ hybridization can be prepared through in vitro transcription from plasmids carrying specific promoter elements and mRNA-specific cDNA, or an alternative polymerase chain reaction (PCR) method can be used through PCR amplification. This chapter describes the procedures for detecting gene expression in zebrafish embryos using whole-mount RNA in situ hybridization.
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Affiliation(s)
- İsmail Ünal
- Department of Biochemistry, Institute of Health Sciences, Marmara University, Istanbul, Turkey
| | - Derya Cansız
- Department of Biochemistry, Faculty of Medicine, Istanbul Medipol University, Istanbul, Turkey
| | - Merih Beler
- Department of Biochemistry, Institute of Health Sciences, Marmara University, Istanbul, Turkey
| | - A Ata Alturfan
- Department of Medical Biochemistry, Faculty of Medicine, Istanbul University-Cerrahpaşa, Istanbul, Turkey
| | - Ebru Emekli-Alturfan
- Department of Basic Medical Sciences, Faculty of Dentistry, Marmara University, Istanbul, Turkey.
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11
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Ünal İ, Beler M, Cansız D, Emekli-Alturfan E. Microarray Analysis to Determine Gene Expression Changes in Zebrafish Embryos. Methods Mol Biol 2024; 2753:563-582. [PMID: 38285368 DOI: 10.1007/978-1-0716-3625-1_37] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2024]
Abstract
Chemical exposure in humans begins from the zygote stage and continues throughout the development of the embryo and the fetus. Zebrafish are one of the most powerful model organisms used in many research areas, including genetics, environmental toxicology, development, DNA damage and repair, cancer, and other diseases. Among the advantages that facilitate the use of zebrafish as a model for studies are features such as high homology with the human genome, small size, and high reproductive potential in short periods. The use of zebrafish embryos in research has increased rapidly due to their advantageous properties, including extrauterine development and the transparent feature of the embryos. However, there are thousands of genes that can be encountered in research, and in this case, the workforce is too much. This workload has been alleviated with the developed technologies. Microarray is one of these technologies. An important parameter in this assay is the RIN value. The RIN value ranges from 1 to 10, indicating mRNA degradation, and therefore helps to decide whether to continue the study. In this chapter, microarray analysis, which is one of the main techniques used in the determination of gene expression in zebrafish embryos, is described.
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Affiliation(s)
- İsmail Ünal
- Department of Biochemistry, Institute of Health Sciences, Marmara University, Istanbul, Turkey
| | - Merih Beler
- Department of Biochemistry, Institute of Health Sciences, Marmara University, Istanbul, Turkey
| | - Derya Cansız
- Department of Biochemistry, Faculty of Medicine, Istanbul Medipol University, Istanbul, Turkey
| | - Ebru Emekli-Alturfan
- Department of Basic Medical Sciences, Faculty of Dentistry, Marmara University, Istanbul, Turkey.
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12
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Singh V, Singh V. Inferring Interaction Networks from Transcriptomic Data: Methods and Applications. Methods Mol Biol 2024; 2812:11-37. [PMID: 39068355 DOI: 10.1007/978-1-0716-3886-6_2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/30/2024]
Abstract
Transcriptomic data is a treasure trove in modern molecular biology, as it offers a comprehensive viewpoint into the intricate nuances of gene expression dynamics underlying biological systems. This genetic information must be utilized to infer biomolecular interaction networks that can provide insights into the complex regulatory mechanisms underpinning the dynamic cellular processes. Gene regulatory networks and protein-protein interaction networks are two major classes of such networks. This chapter thoroughly investigates the wide range of methodologies used for distilling insightful revelations from transcriptomic data that include association-based methods (based on correlation among expression vectors), probabilistic models (using Bayesian and Gaussian models), and interologous methods. We reviewed different approaches for evaluating the significance of interactions based on the network topology and biological functions of the interacting molecules and discuss various strategies for the identification of functional modules. The chapter concludes with highlighting network-based techniques of prioritizing key genes, outlining the centrality-based, diffusion- based, and subgraph-based methods. The chapter provides a meticulous framework for investigating transcriptomic data to uncover assembly of complex molecular networks for their adaptable analyses across a broad spectrum of biological domains.
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Affiliation(s)
- Vikram Singh
- Centre for Computational Biology and Bioinformatics, Central University of Himachal Pradesh, Dharamshala, Himachal Pradesh, India
| | - Vikram Singh
- Centre for Computational Biology and Bioinformatics, Central University of Himachal Pradesh, Dharamshala, Himachal Pradesh, India.
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13
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Yoshida H. Dissecting the Immune System through Gene Regulation. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2024; 1444:219-235. [PMID: 38467983 DOI: 10.1007/978-981-99-9781-7_15] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/13/2024]
Abstract
The immune system plays a dual role in human health, functioning both as a protector against pathogens and, at times, as a contributor to disease. This feature emphasizes the importance to uncover the underlying causes of its malfunctions, necessitating an in-depth analysis in both pathological and physiological conditions to better understand the immune system and immune disorders. Recent advances in scientific technology have enabled extensive investigations into gene regulation, a crucial mechanism governing cellular functionality. Studying gene regulatory mechanisms within the immune system is a promising avenue for enhancing our understanding of immune cells and the immune system as a whole. The gene regulatory mechanisms, revealed through various methodologies, and their implications in the field of immunology are discussed in this chapter.
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Affiliation(s)
- Hideyuki Yoshida
- YCI Laboratory for Immunological Transcriptomics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.
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14
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Stokes T, Cen HH, Kapranov P, Gallagher IJ, Pitsillides AA, Volmar C, Kraus WE, Johnson JD, Phillips SM, Wahlestedt C, Timmons JA. Transcriptomics for Clinical and Experimental Biology Research: Hang on a Seq. ADVANCED GENETICS (HOBOKEN, N.J.) 2023; 4:2200024. [PMID: 37288167 PMCID: PMC10242409 DOI: 10.1002/ggn2.202200024] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Indexed: 06/09/2023]
Abstract
Sequencing the human genome empowers translational medicine, facilitating transcriptome-wide molecular diagnosis, pathway biology, and drug repositioning. Initially, microarrays are used to study the bulk transcriptome; but now short-read RNA sequencing (RNA-seq) predominates. Positioned as a superior technology, that makes the discovery of novel transcripts routine, most RNA-seq analyses are in fact modeled on the known transcriptome. Limitations of the RNA-seq methodology have emerged, while the design of, and the analysis strategies applied to, arrays have matured. An equitable comparison between these technologies is provided, highlighting advantages that modern arrays hold over RNA-seq. Array protocols more accurately quantify constitutively expressed protein coding genes across tissue replicates, and are more reliable for studying lower expressed genes. Arrays reveal long noncoding RNAs (lncRNA) are neither sparsely nor lower expressed than protein coding genes. Heterogeneous coverage of constitutively expressed genes observed with RNA-seq, undermines the validity and reproducibility of pathway analyses. The factors driving these observations, many of which are relevant to long-read or single-cell sequencing are discussed. As proposed herein, a reappreciation of bulk transcriptomic methods is required, including wider use of the modern high-density array data-to urgently revise existing anatomical RNA reference atlases and assist with more accurate study of lncRNAs.
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Affiliation(s)
- Tanner Stokes
- Faculty of ScienceMcMaster UniversityHamiltonL8S 4L8Canada
| | - Haoning Howard Cen
- Life Sciences InstituteUniversity of British ColumbiaVancouverV6T 1Z3Canada
| | | | - Iain J Gallagher
- School of Applied SciencesEdinburgh Napier UniversityEdinburghEH11 4BNUK
| | | | | | | | - James D. Johnson
- Life Sciences InstituteUniversity of British ColumbiaVancouverV6T 1Z3Canada
| | | | | | - James A. Timmons
- Miller School of MedicineUniversity of MiamiMiamiFL33136USA
- William Harvey Research InstituteQueen Mary University LondonLondonEC1M 6BQUK
- Augur Precision Medicine LTDStirlingFK9 5NFUK
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15
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Pruteanu LL, Bender A. Using Transcriptomics and Cell Morphology Data in Drug Discovery: The Long Road to Practice. ACS Med Chem Lett 2023; 14:386-395. [PMID: 37077392 PMCID: PMC10107910 DOI: 10.1021/acsmedchemlett.3c00015] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 03/10/2023] [Indexed: 04/21/2023] Open
Abstract
Gene expression and cell morphology data are high-dimensional biological readouts of much recent interest for drug discovery. They are able to describe biological systems in different states (e.g., healthy and diseased), as well as biological systems before and after compound treatment, and they are hence useful for matching both spaces (e.g., for drug repurposing) as well as for characterizing compounds with respect to efficacy and safety endpoints. This Microperspective describes recent advances in this direction with a focus on applied drug discovery and drug repurposing, as well as outlining what else is needed to advance further, with a particular focus on better understanding the applicability domain of readouts and their relevance for decision making, which is currently often still unclear.
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Affiliation(s)
- Lavinia-Lorena Pruteanu
- Department
of Chemistry and Biology, North University
Center at Baia Mare, Technical University of Cluj-Napoca, Victoriei 76, 430122 Baia Mare, Romania
- Research
Center for Functional Genomics, Biomedicine, and Translational Medicine, “Iuliu Haţieganu” University
of Medicine and Pharmacy, 400337 Cluj-Napoca, Romania
| | - Andreas Bender
- Centre
for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom
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16
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Gurung AB. Human transcriptome profiling: applications in health and disease. TRANSCRIPTOME PROFILING 2023:373-395. [DOI: 10.1016/b978-0-323-91810-7.00020-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/03/2025]
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17
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Creighton CJ. Gene Expression Profiles in Cancers and Their Therapeutic Implications. Cancer J 2023; 29:9-14. [PMID: 36693152 PMCID: PMC9881750 DOI: 10.1097/ppo.0000000000000638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
ABSTRACT The vast amount of gene expression profiling data of bulk tumors and cell lines available in the public domain represents a tremendous resource. For any major cancer type, expression data can identify molecular subtypes, predict patient outcome, identify markers of therapeutic response, determine the functional consequences of somatic mutation, and elucidate the biology of metastatic and advanced cancers. This review provides a broad overview of gene expression profiling in cancer (which may include transcriptome and proteome levels) and the types of findings made using these data. This review also provides specific examples of accessing public cancer gene expression data sets and generating unique views of the data and the resulting genes of interest. These examples involve pan-cancer molecular subtyping, metabolism-associated expression correlates of patient survival involving multiple cancer types, and gene expression correlates of chemotherapy response in breast tumors.
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Affiliation(s)
- Chad J. Creighton
- Dan L. Duncan Comprehensive Cancer Center Division of Biostatistics, Baylor College of Medicine, Houston, TX, USA
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA
- Department of Medicine, Baylor College of Medicine, Houston, TX, USA
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18
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Lüleci HB, Yılmaz A. Robust and rigorous identification of tissue-specific genes by statistically extending tau score. BioData Min 2022; 15:31. [PMID: 36494766 PMCID: PMC9733102 DOI: 10.1186/s13040-022-00315-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Accepted: 11/11/2022] [Indexed: 12/13/2022] Open
Abstract
OBJECTIVES In this study, we aimed to identify tissue-specific genes for various human tissues/organs more robustly and rigorously by extending the tau score algorithm. INTRODUCTION Tissue-specific genes are a class of genes whose functions and expressions are preferred in one or several tissues restrictedly. Identification of tissue-specific genes is essential for discovering multi-cellular biological processes such as tissue-specific molecular regulations, tissue development, physiology, and the pathogenesis of tissue-associated diseases. MATERIALS AND METHODS Gene expression data derived from five large RNA sequencing (RNA-seq) projects, spanning 96 different human tissues, were retrieved from ArrayExpress and ExpressionAtlas. The first step is categorizing genes using significant filters and tau score as a specificity index. After calculating tau for each gene in all datasets separately, statistical distance from the maximum expression level was estimated using a new meaningful procedure. Specific expression of a gene in one or several tissues was calculated after the integration of tau and statistical distance estimation, which is called as extended tau approach. Obtained tissue-specific genes for 96 different human tissues were functionally annotated, and some comparisons were carried out to show the effectiveness of the extended tau method. RESULTS AND DISCUSSION Categorization of genes based on expression level and identification of tissue-specific genes for a large number of tissues/organs were executed. Genes were successfully assigned to multiple tissues by generating the extended tau approach as opposed to the original tau score, which can assign tissue specificity to single tissue only.
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Affiliation(s)
- Hatice Büşra Lüleci
- grid.448834.70000 0004 0595 7127Department of Bioengineering, Gebze Technical University, Kocaeli, Turkey
| | - Alper Yılmaz
- grid.38575.3c0000 0001 2337 3561Department of Bioengineering, Yildiz Technical University, Istanbul, Turkey
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19
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Approaches in Gene Coexpression Analysis in Eukaryotes. BIOLOGY 2022; 11:biology11071019. [PMID: 36101400 PMCID: PMC9312353 DOI: 10.3390/biology11071019] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 06/28/2022] [Accepted: 07/04/2022] [Indexed: 11/22/2022]
Abstract
Simple Summary Genes whose expression levels rise and fall similarly in a large set of samples, may be considered coexpressed. Gene coexpression analysis refers to the en masse discovery of coexpressed genes from a large variety of transcriptomic experiments. The type of biological networks that studies gene coexpression, known as Gene Coexpression Networks, consist of an undirected graph depicting genes and their coexpression relationships. Coexpressed genes are clustered in smaller subnetworks, the predominant biological roles of which can be determined through enrichment analysis. By studying well-annotated gene partners, the attribution of new roles to genes of unknown function or assumption for participation in common metabolic pathways can be achieved, through a guilt-by-association approach. In this review, we present key issues in gene coexpression analysis, as well as the most popular tools that perform it. Abstract Gene coexpression analysis constitutes a widely used practice for gene partner identification and gene function prediction, consisting of many intricate procedures. The analysis begins with the collection of primary transcriptomic data and their preprocessing, continues with the calculation of the similarity between genes based on their expression values in the selected sample dataset and results in the construction and visualisation of a gene coexpression network (GCN) and its evaluation using biological term enrichment analysis. As gene coexpression analysis has been studied extensively, we present most parts of the methodology in a clear manner and the reasoning behind the selection of some of the techniques. In this review, we offer a comprehensive and comprehensible account of the steps required for performing a complete gene coexpression analysis in eukaryotic organisms. We comment on the use of RNA-Seq vs. microarrays, as well as the best practices for GCN construction. Furthermore, we recount the most popular webtools and standalone applications performing gene coexpression analysis, with details on their methods, features and outputs.
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20
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Hephzibah Cathryn R, Udhaya Kumar S, Younes S, Zayed H, George Priya Doss C. A review of bioinformatics tools and web servers in different microarray platforms used in cancer research. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2022; 131:85-164. [PMID: 35871897 DOI: 10.1016/bs.apcsb.2022.05.002] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Over the past decade, conventional lab work strategies have gradually shifted from being limited to a laboratory setting towards a bioinformatics era to help manage and process the vast amounts of data generated by omics technologies. The present work outlines the latest contributions of bioinformatics in analyzing microarray data and their application to cancer. We dissect different microarray platforms and their use in gene expression in cancer models. We highlight how computational advances empowered the microarray technology in gene expression analysis. The study on protein-protein interaction databases classified into primary, derived, meta-database, and prediction databases describes the strategies to curate and predict novel interaction networks in silico. In addition, we summarize the areas of bioinformatics where neural graph networks are currently being used, such as protein functions, protein interaction prediction, and in silico drug discovery and development. We also discuss the role of deep learning as a potential tool in the prognosis, diagnosis, and treatment of cancer. Integrating these resources efficiently, practically, and ethically is likely to be the most challenging task for the healthcare industry over the next decade; however, we believe that it is achievable in the long term.
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Affiliation(s)
- R Hephzibah Cathryn
- Laboratory of Integrative Genomics, Department of Integrative Biology, School of Biosciences and Technology, Vellore Institute of Technology, Vellore, India
| | - S Udhaya Kumar
- Laboratory of Integrative Genomics, Department of Integrative Biology, School of Biosciences and Technology, Vellore Institute of Technology, Vellore, India
| | - Salma Younes
- Department of Biomedical Sciences, College of Health and Sciences, Qatar University, QU Health, Doha, Qatar
| | - Hatem Zayed
- Department of Biomedical Sciences, College of Health and Sciences, Qatar University, QU Health, Doha, Qatar
| | - C George Priya Doss
- Laboratory of Integrative Genomics, Department of Integrative Biology, School of Biosciences and Technology, Vellore Institute of Technology, Vellore, India.
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21
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Hasan S, Huang L, Liu Q, Perlo V, O’Keeffe A, Margarido GRA, Furtado A, Henry RJ. The Long Read Transcriptome of Rice (Oryza sativa ssp. japonica var. Nipponbare) Reveals Novel Transcripts. RICE (NEW YORK, N.Y.) 2022; 15:29. [PMID: 35689714 PMCID: PMC9188635 DOI: 10.1186/s12284-022-00577-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 05/26/2022] [Indexed: 05/08/2023]
Abstract
BACKGROUND High-throughput next-generation sequencing technologies offer a powerful approach to characterizing the transcriptomes of plants. Long read sequencing has been shown to support the discovery of novel isoforms of transcripts. This approach enables the generation of full-length sequences revealing splice variants that may be important in regulating gene action. Investigation of the diversity of transcripts in the rice transcriptome including splice variants was conducted using PacBio long-read sequence data to improve the annotation of the rice genome. RESULTS A cDNA library was prepared from RNA extracted from leaves, roots, seeds, inflorescences, and panicles of O. sativa ssp. japonica var Nipponbare and sequenced on a PacBio Sequel platform. This produced 346,190 non-redundant full-length non-chimeric reads (FLNC) resulting in 33,504 high-quality transcripts. Half of the transcripts were multi-exonic and entirely matched with the reference transcripts. However, 14,874 novel isoforms were also identified resulting predominantly from intron retention and at least one novel splice site. Intron retention was the prevalent alternative splicing event and exon skipping was the least observed. Of 73,659 splice junctions, 12,755 (17%) represented novel splice junctions with canonical and non-canonical intron boundaries. The complexity of the transcriptome was examined in detail for 19 starch synthesis-related genes, defining 276 spliced isoforms of which 94 splice variants were novel. CONCLUSION The data reveal the great complexity of the rice transcriptome. The novel transcripts provide new insights that may be a key input in future research to improve the annotation of the rice genome.
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Affiliation(s)
- Sharmin Hasan
- Queensland Alliance for Agriculture and Food Innovation, University of Queensland, Brisbane, 4072 Australia
- Department of Botany, Jagannath University, Dhaka, 1100 Bangladesh
| | - Lichun Huang
- College of Agriculture, Yangzhou University, Jiangsu, 225009 China
| | - Qiaoquan Liu
- College of Agriculture, Yangzhou University, Jiangsu, 225009 China
| | - Virginie Perlo
- Queensland Alliance for Agriculture and Food Innovation, University of Queensland, Brisbane, 4072 Australia
| | - Angela O’Keeffe
- Queensland Alliance for Agriculture and Food Innovation, University of Queensland, Brisbane, 4072 Australia
| | - Gabriel Rodrigues Alves Margarido
- Departamento de Genética, Escola Superior de Agricultura “Luiz de Queiroz”, Universidade de São Paulo, Piracicaba, São Paulo 13418-900 Brazil
| | - Agnelo Furtado
- Queensland Alliance for Agriculture and Food Innovation, University of Queensland, Brisbane, 4072 Australia
| | - Robert J. Henry
- Queensland Alliance for Agriculture and Food Innovation, University of Queensland, Brisbane, 4072 Australia
- ARC Centre of Excellence for Plant Success in Nature and Agriculture, University of Queensland, Brisbane, 4072 Australia
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22
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Schreiner P, Velasquez MP, Gottschalk S, Zhang J, Fan Y. Unifying heterogeneous expression data to predict targets for CAR-T cell therapy. Oncoimmunology 2021; 10:2000109. [PMID: 34858726 PMCID: PMC8632331 DOI: 10.1080/2162402x.2021.2000109] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Revised: 10/08/2021] [Accepted: 10/26/2021] [Indexed: 10/29/2022] Open
Abstract
Chimeric antigen receptor (CAR) T-cell therapy combines antigen-specific properties of monoclonal antibodies with the lytic capacity of T cells. An effective and safe CAR-T cell therapy strategy relies on identifying an antigen that has high expression and is tumor specific. This strategy has been successfully used to treat patients with CD19+ B-cell acute lymphoblastic leukemia (B-ALL). Finding a suitable target antigen for other cancers such as acute myeloid leukemia (AML) has proven challenging, as the majority of currently targeted AML antigens are also expressed on hematopoietic progenitor cells (HPCs) or mature myeloid cells. Herein, we developed a computational method to perform a data transformation to enable the comparison of publicly available gene expression data across different datasets or assay platforms. The resulting transformed expression values (TEVs) were used in our antigen prediction algorithm to assess suitable tumor-associated antigens (TAAs) that could be targeted with CAR-T cells. We validated this method by identifying B-ALL antigens with known clinical effectiveness, such as CD19 and CD22. Our algorithm predicted TAAs being currently explored preclinically and in clinical CAR-T AML therapy trials, as well as novel TAAs in pediatric megakaryoblastic AML. Thus, this analytical approach presents a promising new strategy to mine diverse datasets for identifying TAAs suitable for immunotherapy.
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Affiliation(s)
- Patrick Schreiner
- The Center for Applied Bioinformatics, St. Jude Children’s Research Hospital, Memphis, TN, USA
| | - Mireya Paulina Velasquez
- Department of Bone Marrow Transplantation and Cell Therapy, St. Jude Children’s Research Hospital, Memphis, TN, USA
| | - Stephen Gottschalk
- Department of Bone Marrow Transplantation and Cell Therapy, St. Jude Children’s Research Hospital, Memphis, TN, USA
| | - Jinghui Zhang
- Department of Computational Biology, St. Jude Children’s Research Hospital, Memphis, TN, USA
| | - Yiping Fan
- The Center for Applied Bioinformatics, St. Jude Children’s Research Hospital, Memphis, TN, USA
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23
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Puła A, Robak P, Mikulski D, Robak T. The Significance of mRNA in the Biology of Multiple Myeloma and Its Clinical Implications. Int J Mol Sci 2021; 22:12070. [PMID: 34769503 PMCID: PMC8584466 DOI: 10.3390/ijms222112070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2021] [Revised: 10/28/2021] [Accepted: 11/04/2021] [Indexed: 11/16/2022] Open
Abstract
Multiple myeloma (MM) is a genetically complex disease that results from a multistep transformation of normal to malignant plasma cells in the bone marrow. However, the molecular mechanisms responsible for the initiation and heterogeneous evolution of MM remain largely unknown. A fundamental step needed to understand the oncogenesis of MM and its response to therapy is the identification of driver mutations. The introduction of gene expression profiling (GEP) in MM is an important step in elucidating the molecular heterogeneity of MM and its clinical relevance. Since some mutations in myeloma occur in non-coding regions, studies based on the analysis of mRNA provide more comprehensive information on the oncogenic pathways and mechanisms relevant to MM biology. In this review, we discuss the role of gene expression profiling in understanding the biology of multiple myeloma together with the clinical manifestation of the disease, as well as its impact on treatment decisions and future directions.
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Affiliation(s)
- Anna Puła
- Department of Hematology, Medical University of Lodz, 93-510 Lodz, Poland;
| | - Paweł Robak
- Department of Experimental Hematology, Medical University of Lodz, 93-510 Lodz, Poland;
| | - Damian Mikulski
- Department of Biostatistics and Translational Medicine, Medical University of Lodz, 92-215 Lodz, Poland;
| | - Tadeusz Robak
- Department of Hematology, Medical University of Lodz, 93-510 Lodz, Poland;
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24
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Chen B, Gao L, Shang X. A two-way rectification method for identifying differentially expressed genes by maximizing the co-function relationship. BMC Genomics 2021; 22:471. [PMID: 34171992 PMCID: PMC8229713 DOI: 10.1186/s12864-021-07772-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2020] [Accepted: 06/04/2021] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND The identification of differentially expressed genes (DEGs) is an important task in many biological studies. The currently widely used methods often calculate a score for each gene by estimating the significance level in terms of the differential expression. However, biological experiments often have only three duplications, plus plenty of noises contain in gene expression datasets, which brings a great challenge to statistical analysis methods. Moreover, the abundance of gene expression levels are not evenly distributed. Thus, those low expressed genes are more easily to be detected by fold-change based methods, which may results in high false positives among the DEG list. Since phenotypical changes result from DEGs should be strongly related to several distinct cellular functions, a more robust method should be designed to increase the true positive rate of the functional related DEGs. RESULTS In this study, we propose a two-way rectification method for identifying DEGs by maximizing the co-function relationships between genes and their enriched cellular pathways. An iteration strategy is employed to sequentially narrow down the group of identified DEGs and their associated biological functions. Functional analyses reveal that the identified DEGs are well organized in the form of functional modules, and the enriched pathways are very significant with lower p-value and larger gene count. CONCLUSIONS An integrative rectification method was proposed to identify key DEGs and their related functions simultaneously. The experimental validations demonstrate that the method has high interpretability and feasibility. It performs very well in terms of the identification of remarkable functional related genes.
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Affiliation(s)
- Bolin Chen
- School of Computer Science, Northwestern Polytechnical University, 127 Youyi west road, Xi’an, 710072 China
- Key Laboratory of Big Data Storage and Management, Ministry of Industry and Information Technology, 127 Youyi west road, Xi’an, 710072 China
- Centre for Multidisciplinary Convergence Computing (CMCC), 127 Youyi west road, Xi’an, 710072 China
- National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, 127 Youyi west road, Xi’an, 710072 China
| | - Li Gao
- School of Software, Northwestern Polytechnical University, 127 Youyi west road, Xi’an, 710072 China
| | - Xuequn Shang
- School of Computer Science, Northwestern Polytechnical University, 127 Youyi west road, Xi’an, 710072 China
- Key Laboratory of Big Data Storage and Management, Ministry of Industry and Information Technology, 127 Youyi west road, Xi’an, 710072 China
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25
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Zhu GD, Cao XJ, Li YP, Li JX, Leng ZJ, Xie LM, Guo XG. Identification of differentially expressed genes and signaling pathways in human conjunctiva and reproductive tract infected with Chlamydia trachomatis. Hum Genomics 2021; 15:22. [PMID: 33875006 PMCID: PMC8056519 DOI: 10.1186/s40246-021-00313-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Accepted: 02/09/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Currently, Chlamydia trachomatis-specific host defense mechanisms in humans remain poorly defined. To study the characteristics of host cells infected early with Chlamydia trachomatis, we used bioinformatics methods to analyze the RNA transcription profiles of the conjunctiva, fallopian tubes, and endometrium in humans infected with Chlamydia trachomatis. METHOD The gene expression profiles of GSE20430, GSE20436, GSE26692, and GSE41075 were downloaded from the Gene Expression Synthesis (GEO) database. Then, we obtained the differentially expressed genes (DEGs) through the R 4.0.1 software. STRING was used to construct protein-protein interaction (PPI) networks; then, the Cytoscape 3.7.2 software was used to visualize the PPI and screen hub genes. GraphPad Prism 8.0 software was used to verify the expression of the hub gene. In addition, the gene-miRNA interaction was constructed on the NetworkAnalyst 3.0 platform using the miRTarBase v8.0 database. RESULTS A total of 600 and 135 DEGs were screened out in the conjunctival infection group and the reproductive tract infection group, respectively. After constructing a PPI network and verifying the hub genes, CSF2, CD40, and CSF3 in the reproductive tract infection group proved to have considerable statistical significance. CONCLUSION In our research, the key genes in the biological process of reproductive tract infection with Chlamydia trachomatis were clarified through bioinformatics analysis. These hub genes may be further used in clinical treatment and clinical diagnosis.
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Affiliation(s)
- Guo-Dong Zhu
- Departments of Geriatrics and Oncology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, 510180, Guangdong, China
| | - Xun-Jie Cao
- Department of Clinical Laboratory Medicine, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510150, China
- Department of Clinical Medicine, The Third Clinical School of Guangzhou Medical University, Guangzhou, 511436, China
| | - Ya-Ping Li
- Department of Clinical Laboratory Medicine, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510150, China
- Department of Clinical Medicine, The Third Clinical School of Guangzhou Medical University, Guangzhou, 511436, China
- Department of Clinical Medicine, The Second Clinical School of Guangzhou Medical University, Guangzhou, 511436, China
| | - Jia-Xin Li
- Department of Clinical Laboratory Medicine, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510150, China
- Department of Clinical Medicine, The Third Clinical School of Guangzhou Medical University, Guangzhou, 511436, China
| | - Zi-Jian Leng
- Department of Clinical Laboratory Medicine, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510150, China
- Department of Clinical Medicine, The Third Clinical School of Guangzhou Medical University, Guangzhou, 511436, China
| | - Li-Min Xie
- Department of Clinical Laboratory Medicine, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510150, China
- Department of Clinical Medicine, The Third Clinical School of Guangzhou Medical University, Guangzhou, 511436, China
| | - Xu-Guang Guo
- Department of Clinical Laboratory Medicine, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510150, China.
- Department of Clinical Medicine, The Third Clinical School of Guangzhou Medical University, Guangzhou, 511436, China.
- Key Laboratory for Major Obstetric Diseases of Guangdong Province, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, ,510150, China.
- Key Laboratory of Reproduction and Genetics of Guangdong Higher Education Institutes, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510150, China.
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26
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Asplund O, Rung J, Groop L, Prasad B R, Hansson O. MuscleAtlasExplorer: a web service for studying gene expression in human skeletal muscle. Database (Oxford) 2020; 2020:baaa111. [PMID: 33338203 PMCID: PMC7747357 DOI: 10.1093/database/baaa111] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Revised: 10/13/2020] [Accepted: 12/14/2020] [Indexed: 01/30/2023]
Abstract
MuscleAtlasExplorer is a freely available web application that allows for the exploration of gene expression data from human skeletal muscle. It draws from an extensive publicly available dataset of 1654 skeletal muscle expression microarray samples. Detailed, manually curated, patient phenotype data, with information such as age, sex, BMI and disease status, are combined with skeletal muscle gene expression to provide insights into gene function in skeletal muscle. It aims to facilitate easy exploration of the data using powerful data visualization functions, while allowing for sample selection, in-depth inspection and further analysis using external tools. Availability: MuscleAtlasExplorer is available at https://mae.crc.med.lu.se/mae2 (username 'muscle' and password 'explorer' pre-publication).
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Affiliation(s)
- Olof Asplund
- Genomics, Diabetes and Endocrinology Unit, Department of Clinical Sciences, Lund University Diabetes Centre, Jan Waldenströms gata 35, Malmö 20502, Sweden
| | - Johan Rung
- SciLifeLab, BMC, Husargatan 3, Uppsala University, Uppsala 751 22, Sweden
| | - Leif Groop
- Genomics, Diabetes and Endocrinology Unit, Department of Clinical Sciences, Lund University Diabetes Centre, Jan Waldenströms gata 35, Malmö 20502, Sweden
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Tukholmankatu 800290 Helsinki, Finland
| | - Rashmi Prasad B
- Genomics, Diabetes and Endocrinology Unit, Department of Clinical Sciences, Lund University Diabetes Centre, Jan Waldenströms gata 35, Malmö 20502, Sweden
| | - Ola Hansson
- Genomics, Diabetes and Endocrinology Unit, Department of Clinical Sciences, Lund University Diabetes Centre, Jan Waldenströms gata 35, Malmö 20502, Sweden
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Tukholmankatu 800290 Helsinki, Finland
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Fun(gi)omics: Advanced and Diverse Technologies to Explore Emerging Fungal Pathogens and Define Mechanisms of Antifungal Resistance. mBio 2020; 11:mBio.01020-20. [PMID: 33024032 PMCID: PMC7542357 DOI: 10.1128/mbio.01020-20] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
The landscape of infectious fungal agents includes previously unidentified or rare pathogens with the potential to cause unprecedented casualties in biodiversity, food security, and human health. The influences of human activity, including the crisis of climate change, along with globalized transport, are underlying factors shaping fungal adaptation to increased temperature and expanded geographical regions. Furthermore, the emergence of novel antifungal-resistant strains linked to excessive use of antifungals (in the clinic) and fungicides (in the field) offers an additional challenge to protect major crop staples and control dangerous fungal outbreaks. The landscape of infectious fungal agents includes previously unidentified or rare pathogens with the potential to cause unprecedented casualties in biodiversity, food security, and human health. The influences of human activity, including the crisis of climate change, along with globalized transport, are underlying factors shaping fungal adaptation to increased temperature and expanded geographical regions. Furthermore, the emergence of novel antifungal-resistant strains linked to excessive use of antifungals (in the clinic) and fungicides (in the field) offers an additional challenge to protect major crop staples and control dangerous fungal outbreaks. Hence, the alarming frequency of fungal infections in medical and agricultural settings requires effective research to understand the virulent nature of fungal pathogens and improve the outcome of infection in susceptible hosts. Mycology-driven research has benefited from a contemporary and unified approach of omics technology, deepening the biological, biochemical, and biophysical understanding of these emerging fungal pathogens. Here, we review the current state-of-the-art multi-omics technologies, explore the power of data integration strategies, and highlight discovery-based revelations of globally important and taxonomically diverse fungal pathogens. This information provides new insight for emerging pathogens through an in-depth understanding of well-characterized fungi and provides alternative therapeutic strategies defined through novel findings of virulence, adaptation, and resistance.
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28
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Pathway identification through transcriptome analysis. Cell Signal 2020; 74:109701. [PMID: 32649993 DOI: 10.1016/j.cellsig.2020.109701] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Revised: 06/24/2020] [Accepted: 06/24/2020] [Indexed: 12/18/2022]
Abstract
Systems-based, agnostic approaches focusing on transcriptomics data have been employed to understand the pathogenesis of polycystic kidney diseases (PKD). While multiple signaling pathways, including Wnt, mTOR and G-protein-coupled receptors, have been implicated in late stages of disease, there were few insights into the transcriptional cascade immediately downstream of Pkd1 inactivation. One of the consistent findings has been transcriptional evidence of dysregulated metabolic and cytoskeleton remodeling pathways. Recent technical developments, including bulk and single-cell RNA sequencing technologies and spatial transcriptomics, offer new angles to investigate PKD. In this article, we review what has been learned based on transcriptional approaches and consider future opportunities.
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Bushel PR, Ferguson SS, Ramaiahgari SC, Paules RS, Auerbach SS. Comparison of Normalization Methods for Analysis of TempO-Seq Targeted RNA Sequencing Data. Front Genet 2020; 11:594. [PMID: 32655620 PMCID: PMC7325690 DOI: 10.3389/fgene.2020.00594] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2020] [Accepted: 05/15/2020] [Indexed: 11/30/2022] Open
Abstract
Analysis of bulk RNA sequencing (RNA-Seq) data is a valuable tool to understand transcription at the genome scale. Targeted sequencing of RNA has emerged as a practical means of assessing the majority of the transcriptomic space with less reliance on large resources for consumables and bioinformatics. TempO-Seq is a templated, multiplexed RNA-Seq platform that interrogates a panel of sentinel genes representative of genome-wide transcription. Nuances of the technology require proper preprocessing of the data. Various methods have been proposed and compared for normalizing bulk RNA-Seq data, but there has been little to no investigation of how the methods perform on TempO-Seq data. We simulated count data into two groups (treated vs. untreated) at seven-fold change (FC) levels (including no change) using control samples from human HepaRG cells run on TempO-Seq and normalized the data using seven normalization methods. Upper Quartile (UQ) performed the best with regard to maintaining FC levels as detected by a limma contrast between treated vs. untreated groups. For all FC levels, specificity of the UQ normalization was greater than 0.84 and sensitivity greater than 0.90 except for the no change and +1.5 levels. Furthermore, K-means clustering of the simulated genes normalized by UQ agreed the most with the FC assignments [adjusted Rand index (ARI) = 0.67]. Despite having an assumption of the majority of genes being unchanged, the DESeq2 scaling factors normalization method performed reasonably well as did simple normalization procedures counts per million (CPM) and total counts (TCs). These results suggest that for two class comparisons of TempO-Seq data, UQ, CPM, TC, or DESeq2 normalization should provide reasonably reliable results at absolute FC levels ≥2.0. These findings will help guide researchers to normalize TempO-Seq gene expression data for more reliable results.
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Affiliation(s)
- Pierre R Bushel
- Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences of National Institutes of Health, Durham, NC, United States.,Massive Genome Informatics Group, National Institute of Environmental Health Sciences of National Institutes of Health, Durham, NC, United States.,Biomolecular Screening Branch, National Institute of Environmental Health Sciences of National Institutes of Health, Durham, NC, United States
| | - Stephen S Ferguson
- Biomolecular Screening Branch, National Institute of Environmental Health Sciences of National Institutes of Health, Durham, NC, United States
| | - Sreenivasa C Ramaiahgari
- Biomolecular Screening Branch, National Institute of Environmental Health Sciences of National Institutes of Health, Durham, NC, United States
| | - Richard S Paules
- Biomolecular Screening Branch, National Institute of Environmental Health Sciences of National Institutes of Health, Durham, NC, United States
| | - Scott S Auerbach
- Biomolecular Screening Branch, National Institute of Environmental Health Sciences of National Institutes of Health, Durham, NC, United States
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Malatras A, Michalopoulos I, Duguez S, Butler-Browne G, Spuler S, Duddy WJ. MyoMiner: explore gene co-expression in normal and pathological muscle. BMC Med Genomics 2020; 13:67. [PMID: 32393257 PMCID: PMC7216615 DOI: 10.1186/s12920-020-0712-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Accepted: 04/13/2020] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND High-throughput transcriptomics measures mRNA levels for thousands of genes in a biological sample. Most gene expression studies aim to identify genes that are differentially expressed between different biological conditions, such as between healthy and diseased states. However, these data can also be used to identify genes that are co-expressed within a biological condition. Gene co-expression is used in a guilt-by-association approach to prioritize candidate genes that could be involved in disease, and to gain insights into the functions of genes, protein relations, and signaling pathways. Most existing gene co-expression databases are generic, amalgamating data for a given organism regardless of tissue-type. METHODS To study muscle-specific gene co-expression in both normal and pathological states, publicly available gene expression data were acquired for 2376 mouse and 2228 human striated muscle samples, and separated into 142 categories based on species (human or mouse), tissue origin, age, gender, anatomic part, and experimental condition. Co-expression values were calculated for each category to create the MyoMiner database. RESULTS Within each category, users can select a gene of interest, and the MyoMiner web interface will return all correlated genes. For each co-expressed gene pair, adjusted p-value and confidence intervals are provided as measures of expression correlation strength. A standardized expression-level scatterplot is available for every gene pair r-value. MyoMiner has two extra functions: (a) a network interface for creating a 2-shell correlation network, based either on the most highly correlated genes or from a list of genes provided by the user with the option to include linked genes from the database and (b) a comparison tool from which the users can test whether any two correlation coefficients from different conditions are significantly different. CONCLUSIONS These co-expression analyses will help investigators to delineate the tissue-, cell-, and pathology-specific elements of muscle protein interactions, cell signaling and gene regulation. Changes in co-expression between pathologic and healthy tissue may suggest new disease mechanisms and help define novel therapeutic targets. Thus, MyoMiner is a powerful muscle-specific database for the discovery of genes that are associated with related functions based on their co-expression. MyoMiner is freely available at https://www.sys-myo.com/myominer.
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Affiliation(s)
- Apostolos Malatras
- Sorbonne Université, Inserm, Institut de Myologie, U974, Center for Research in Myology, 47 Boulevard de l’hôpital, 75013 Paris, France
| | - Ioannis Michalopoulos
- Centre of Systems Biology, Biomedical Research Foundation, Academy of Athens, 4 Soranou Ephessiou St., 11527 Athens, Greece
| | - Stéphanie Duguez
- Sorbonne Université, Inserm, Institut de Myologie, U974, Center for Research in Myology, 47 Boulevard de l’hôpital, 75013 Paris, France
- Northern Ireland Centre for Stratified Medicine, Biomedical Sciences Research Institute, C-TRIC, Altnagelvin Hospital Campus, Glenshane Road, Ulster University, Derry/Londonderry, BT47 6SB UK
| | - Gillian Butler-Browne
- Sorbonne Université, Inserm, Institut de Myologie, U974, Center for Research in Myology, 47 Boulevard de l’hôpital, 75013 Paris, France
| | - Simone Spuler
- Muscle Research Unit, Experimental and Clinical Research Center – a joint cooperation of the Charité Medical Faculty and the Max Delbrück Center for Molecular Medicine, Lindenberger Weg 80, 13125 Berlin, Germany
| | - William J. Duddy
- Sorbonne Université, Inserm, Institut de Myologie, U974, Center for Research in Myology, 47 Boulevard de l’hôpital, 75013 Paris, France
- Northern Ireland Centre for Stratified Medicine, Biomedical Sciences Research Institute, C-TRIC, Altnagelvin Hospital Campus, Glenshane Road, Ulster University, Derry/Londonderry, BT47 6SB UK
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Pradeep C, Nandan D, Das AA, Velayutham D. Comparative Transcriptome Profiling of Disruptive Technology, Single- Molecule Direct RNA Sequencing. Curr Bioinform 2020. [DOI: 10.2174/1574893614666191017154427] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Background:
The standard approach for transcriptomic profiling involves high
throughput short-read sequencing technology, mainly dominated by Illumina. However, the short
reads have limitations in transcriptome assembly and in obtaining full-length transcripts due to the
complex nature of transcriptomes with variable length and multiple alternative spliced isoforms.
Recent advances in long read sequencing by the Oxford Nanopore Technologies (ONT) offered
both cDNA as well as direct RNA sequencing and has brought a paradigm change in the
sequencing technology to greatly improve the assembly and expression estimates. ONT enables
molecules to be sequenced without fragmentation resulting in ultra-long read length enabling the
entire genes and transcripts to be fully characterized. The direct RNA sequencing method, in
addition, circumvents the reverse transcription and amplification steps.
Objective:
In this study, RNA sequencing methods were assessed by comparing data from Illumina
(ILM), ONT cDNA (OCD) and ONT direct RNA (ODR).
Methods:
The sensitivity & specificity of the isoform detection was determined from the data
generated by Illumina, ONT cDNA and ONT direct RNA sequencing technologies using
Saccharomyces cerevisiae as model. Comparative studies were conducted with two pipelines to
detect the isoforms, novel genes and variable gene length.
Results:
Mapping metrics and qualitative profiles for different pipelines are presented to
understand these disruptive technologies. The variability in sequencing technology and the
analysis pipeline were studied.
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Affiliation(s)
- Chaithra Pradeep
- Bioinformatics Team, AgriGenome Labs Pvt Ltd, Kakkanad, Kerala, India
| | - Dharam Nandan
- Bioinformatics Team, AgriGenome Labs Pvt Ltd, Kakkanad, Kerala, India
| | - Arya A. Das
- Bioinformatics Team, AgriGenome Labs Pvt Ltd, Kakkanad, Kerala, India
| | - Dinesh Velayutham
- Bioinformatics Team, AgriGenome Labs Pvt Ltd, Kakkanad, Kerala, India
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Rochette-Egly C. Retinoic Acid-Regulated Target Genes During Development: Integrative Genomics Analysis. Subcell Biochem 2020; 95:57-85. [PMID: 32297296 DOI: 10.1007/978-3-030-42282-0_3] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Retinoic acid (RA), a major natural active metabolite of vitamin A (VA) is well known to play critical roles in embryonic development. The effects of RA are mediated by nuclear receptors (RARs), which regulate the expression of gene batteries involved in cell growth and differentiation. Since the early 1990s several laboratories have focused on understanding how RA-regulated genes and RAR binding sites operate by studying the differentiation of embryonal carcinoma cells and embryonic stem cells. The development of hybridization-based microarray technology and high performance software analysis programs has allowed the characterization of thousands of RA-regulated genes. During the two last decades, publication of the genome sequence of various organisms has allowed advances in massive parallel sequencing and bioinformatics analysis of genome-wide data sets. These new generation sequencing (NGS) technologies have revolutionized the field by providing a global integrated picture of RA-regulated gene networks and the regulatory programs involved in cell fate decisions during embryonal carcinoma and embryonic stem cells differentiation. Now the challenge is to reconstruct the RA-regulated gene networks at the single cell level during the development of specialized embryonic tissues.
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Affiliation(s)
- Cecile Rochette-Egly
- Université de Strasbourg, IGBMC (Institut de Génétique et de Biologie Moléculaire et Cellulaire), INSERM, U964, CNRS, UMR7104, 1 rue Laurent Fries, BP 10142, 67404, Illkirch Cedex, France.
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33
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Overcoming challenges and dogmas to understand the functions of pseudogenes. Nat Rev Genet 2019; 21:191-201. [DOI: 10.1038/s41576-019-0196-1] [Citation(s) in RCA: 92] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/05/2019] [Indexed: 01/08/2023]
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Byrne A, Cole C, Volden R, Vollmers C. Realizing the potential of full-length transcriptome sequencing. Philos Trans R Soc Lond B Biol Sci 2019; 374:20190097. [PMID: 31587638 PMCID: PMC6792442 DOI: 10.1098/rstb.2019.0097] [Citation(s) in RCA: 87] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/24/2019] [Indexed: 12/27/2022] Open
Abstract
Long-read sequencing holds great potential for transcriptome analysis because it offers researchers an affordable method to annotate the transcriptomes of non-model organisms. This, in turn, will greatly benefit future work on less-researched organisms like unicellular eukaryotes that cannot rely on large consortia to generate these transcriptome annotations. However, to realize this potential, several remaining molecular and computational challenges will have to be overcome. In this review, we have outlined the limitations of short-read sequencing technology and how long-read sequencing technology overcomes these limitations. We have also highlighted the unique challenges still present for long-read sequencing technology and provided some suggestions on how to overcome these challenges going forward. This article is part of a discussion meeting issue 'Single cell ecology'.
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Affiliation(s)
- Ashley Byrne
- Department of Molecular, Cellular, and Developmental Biology, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Charles Cole
- Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Roger Volden
- Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Christopher Vollmers
- Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, USA
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35
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Xie S, Braga-Neto UM. On the Bias of Precision Estimation Under Separate Sampling. Cancer Inform 2019; 18:1176935119860822. [PMID: 31360060 PMCID: PMC6636226 DOI: 10.1177/1176935119860822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2019] [Accepted: 06/02/2019] [Indexed: 11/29/2022] Open
Abstract
Observational case-control studies for biomarker discovery in cancer studies often collect data that are sampled separately from the case and control populations. We present an analysis of the bias in the estimation of the precision of classifiers designed on separately sampled data. The analysis consists of both theoretical and numerical results, which show that classifier precision estimates can display strong bias under separating sampling, with the bias magnitude depending on the difference between the true case prevalence in the population and the sample prevalence in the data. We show that this bias is systematic in the sense that it cannot be reduced by increasing sample size. If information about the true case prevalence is available from public health records, then a modified precision estimator that uses the known prevalence displays smaller bias, which can in fact be reduced to zero as sample size increases under regularity conditions on the classification algorithm. The accuracy of the theoretical analysis and the performance of the precision estimators under separate sampling are confirmed by numerical experiments using synthetic and real data from published observational case-control studies. The results with real data confirmed that under separately sampled data, the usual estimator produces larger, ie, more optimistic, precision estimates than the estimator using the true prevalence value.
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Affiliation(s)
- Shuilian Xie
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA
| | - Ulisses M Braga-Neto
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA
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36
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Li J, Seo B, Lin L. Optimal transport, mean partition, and uncertainty assessment in cluster analysis. Stat Anal Data Min 2019. [DOI: 10.1002/sam.11418] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Jia Li
- Department of Statistics The Pennsylvania State University University Park Pennsylvania
| | - Beomseok Seo
- Department of Statistics The Pennsylvania State University University Park Pennsylvania
| | - Lin Lin
- Department of Statistics The Pennsylvania State University University Park Pennsylvania
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37
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Taneja G, Maity S, Jiang W, Moorthy B, Coarfa C, Ghose R. Transcriptomic profiling identifies novel mechanisms of transcriptional regulation of the cytochrome P450 (Cyp)3a11 gene. Sci Rep 2019; 9:6663. [PMID: 31040347 PMCID: PMC6491424 DOI: 10.1038/s41598-019-43248-w] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2018] [Accepted: 04/04/2019] [Indexed: 02/06/2023] Open
Abstract
Cytochrome P450 (CYP)3A is the most abundant CYP enzyme in the human liver, and a functional impairment of this enzyme leads to unanticipated adverse reactions and therapeutic failures; these reactions result in the early termination of drug development or the withdrawal of drugs from the market. The transcriptional regulation mechanism of the Cyp3a gene is not fully understood and requires a thorough investigation. We mapped the transcriptome of the Cyp3a gene in a mouse model. The Cyp3a gene was induced using the mPXR activator pregnenolone-16alpha-carbonitrile (PCN) and was subsequently downregulated using lipopolysaccharide (LPS). Our objective was to identify the transcription factors (TFs), epigenetic modulators and molecular pathways that are enriched or repressed by PCN and LPS based on a gene set enrichment analysis. Our analysis shows that 113 genes were significantly upregulated (by at least 1.5-fold) with PCN treatment, and that 834 genes were significantly downregulated (by at least 1.5-fold) with LPS treatment. Additionally, the targets of the 536 transcription factors were enriched by a combined treatment of PCN and LPS, and among these, 285 were found to have binding sites on Cyp3a11. Moreover, the repressed targets of the epigenetic markers HDAC1, HDAC3 and EZH2 were further suppressed by LPS treatment and were enhanced by PCN treatment. By identifying and contrasting the transcriptional regulators that are altered by PCN and LPS, our study provides novel insights into the transcriptional regulation of CYP3A in the liver.
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Affiliation(s)
- Guncha Taneja
- Department of Pharmacological and Pharmaceutical Sciences, University of Houston, 4849 Calhoun Rd., Houston, TX, 77204, USA
- DILIsym Services, A Simulations Plus Company, Research Triangle Park, North Carolina, 27709, USA
| | - Suman Maity
- Advanced Technology Cores, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA
| | - Weiwu Jiang
- Department of Pediatrics, Section of Neonatology, Texas Children's Hospital, Baylor College of Medicine, 1102 Bates Avenue, Suite 530, Houston, TX, 77030, USA
| | - Bhagavatula Moorthy
- Department of Pediatrics, Section of Neonatology, Texas Children's Hospital, Baylor College of Medicine, 1102 Bates Avenue, Suite 530, Houston, TX, 77030, USA.
| | - Cristian Coarfa
- Dan L Duncan Comprehensive Cancer Center, Center for Precision Environmental Health, Molecular and Cellular Biology Department, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA.
| | - Romi Ghose
- Department of Pharmacological and Pharmaceutical Sciences, University of Houston, 4849 Calhoun Rd., Houston, TX, 77204, USA.
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38
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Wang B, Kumar V, Olson A, Ware D. Reviving the Transcriptome Studies: An Insight Into the Emergence of Single-Molecule Transcriptome Sequencing. Front Genet 2019; 10:384. [PMID: 31105749 PMCID: PMC6498185 DOI: 10.3389/fgene.2019.00384] [Citation(s) in RCA: 74] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2018] [Accepted: 04/09/2019] [Indexed: 12/23/2022] Open
Abstract
Advances in transcriptomics have provided an exceptional opportunity to study functional implications of the genetic variability. Technologies such as RNA-Seq have emerged as state-of-the-art techniques for transcriptome analysis that take advantage of high-throughput next-generation sequencing. However, similar to their predecessors, these approaches continue to impose major challenges on full-length transcript structure identification, primarily due to inherent limitations of read length. With the development of single-molecule sequencing (SMS) from PacBio, a growing number of studies on the transcriptome of different organisms have been reported. SMS has emerged as advantageous for comprehensive genome annotation including identification of novel genes/isoforms, long non-coding RNAs and fusion transcripts. This approach can be used across a broad spectrum of species to better interpret the coding information of the genome, and facilitate the biological function study. We provide an overview of SMS platform and its diverse applications in various biological studies, and our perspective on the challenges associated with the transcriptome studies.
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Affiliation(s)
- Bo Wang
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, United States
| | - Vivek Kumar
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, United States
| | - Andrew Olson
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, United States
| | - Doreen Ware
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, United States.,USDA-ARS Robert W. Holley Center for Agriculture and Health, Ithaca, NY, United States
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39
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Mar JC. The rise of the distributions: why non-normality is important for understanding the transcriptome and beyond. Biophys Rev 2019; 11:89-94. [PMID: 30617454 PMCID: PMC6381358 DOI: 10.1007/s12551-018-0494-4] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2018] [Accepted: 12/17/2018] [Indexed: 01/08/2023] Open
Abstract
The application of statistics has been instrumental in clarifying our understanding of the genome. While insights have been derived for almost all levels of genome function, most importantly, statistics has had the greatest impact on improving our knowledge of transcriptional regulation. But the drive to extract the most meaningful inferences from big data can often force us to overlook the fundamental role that statistics plays, and specifically, the basic assumptions that we make about big data. Normality is a statistical property that is often swept up into an assumption that we may or may not be consciously aware of making. This review highlights the inherent value of non-normal distributions to big data analysis by discussing use cases of non-normality that focus on gene expression data. Collectively, these examples help to motivate the premise of why at this stage, now more than ever, non-normality is important for learning about gene regulation, transcriptomics, and more.
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Affiliation(s)
- Jessica C Mar
- Australian Institute for Bioengineering and Nanotechnology, University of Queensland, QLD, Brisbane, 4072, Australia.
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40
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Hovhannisyan H, Gabaldón T. Transcriptome Sequencing Approaches to Elucidate Host-Microbe Interactions in Opportunistic Human Fungal Pathogens. Curr Top Microbiol Immunol 2019; 422:193-235. [PMID: 30128828 DOI: 10.1007/82_2018_122] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Infections caused by opportunistic human fungal pathogens are a source of increasing medical concern, due to their growing incidence, the emergence of novel pathogenic species, and the lack of effective diagnostics tools. Fungal pathogens are phylogenetically diverse, and their virulence mechanisms can differ widely across species. Despite extensive efforts, the molecular bases of virulence in pathogenic fungi and their interactions with the human host remain poorly understood for most species. In this context, next-generation sequencing approaches hold the promise of helping to close this knowledge gap. In particular, high-throughput transcriptome sequencing (RNA-Seq) enables monitoring the transcriptional profile of both host and microbes to elucidate their interactions and discover molecular mechanisms of virulence and host defense. Here, we provide an overview of transcriptome sequencing techniques and approaches, and survey their application in studying the interplay between humans and fungal pathogens. Finally, we discuss novel RNA-Seq approaches in studying host-pathogen interactions and their potential role in advancing the clinical diagnostics of fungal infections.
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Affiliation(s)
- Hrant Hovhannisyan
- Centre for Genomic Regulation, The Barcelona Institute of Science and Technology, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
| | - Toni Gabaldón
- Centre for Genomic Regulation, The Barcelona Institute of Science and Technology, Barcelona, Spain.
- Universitat Pompeu Fabra, Barcelona, Spain.
- Institució Catalana de Recerca i Estudis Avançats, Barcelona, Spain.
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Wu L, Qiu X, Yuan YX, Wu H. Parameter Estimation and Variable Selection for Big Systems of Linear Ordinary Differential Equations: A Matrix-Based Approach. J Am Stat Assoc 2019; 114:657-667. [PMID: 34385718 DOI: 10.1080/01621459.2017.1423074] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
Ordinary differential equations (ODEs) are widely used to model the dynamic behavior of a complex system. Parameter estimation and variable selection for a "Big System" with linear ODEs are very challenging due to the need of nonlinear optimization in an ultra-high dimensional parameter space. In this article, we develop a parameter estimation and variable selection method based on the ideas of similarity transformation and separable least squares (SLS). Simulation studies demonstrate that the proposed matrix-based SLS method could be used to estimate the coefficient matrix more accurately and perform variable selection for a linear ODE system with thousands of dimensions and millions of parameters much better than the direct least squares (LS) method and the vector-based two-stage method that are currently available. We applied this new method to two real data sets: a yeast cell cycle gene expression data set with 30 dimensions and 930 unknown parameters and the Standard & Poor 1500 index stock price data with 1250 dimensions and 1,563,750 unknown parameters, to illustrate the utility and numerical performance of the proposed parameter estimation and variable selection method for big systems in practice.
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Affiliation(s)
- Leqin Wu
- Department of Mathematics, Jinan University, Guangzhou, China
| | - Xing Qiu
- Department of Biostatistics and Computational Biology University of Rochester, Rochester, New York, U.S.A
| | - Ya-Xiang Yuan
- Academy of Mathematics and System Sciences Chinese Academy of Sciences, Beijing, China
| | - Hulin Wu
- Department of Biostatistics, University of Texas Health Science Center at Houston, Houston, TX, U.S.A
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Kashyap S, Kumar S, Agarwal V, Misra DP, Phadke SR, Kapoor A. Protein protein interaction network analysis of differentially expressed genes to understand involved biological processes in coronary artery disease and its different severity. GENE REPORTS 2018; 12:50-60. [DOI: 10.1016/j.genrep.2018.05.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
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Kashyap S, Kumar S, Agarwal V, Misra DP, Phadke SR, Kapoor A. Gene expression profiling of coronary artery disease and its relation with different severities. J Genet 2018; 97:853-867. [PMID: 30262697 DOI: 10.1007/s12041-018-0980-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2017] [Revised: 12/30/2017] [Accepted: 01/11/2018] [Indexed: 12/19/2022]
Abstract
Global gene expression profiling is a powerful tool enabling the understanding of pathophysiology and subsequent management of diseases. This study aims to explore functionally annotated differentially expressed genes (DEGs); their biological processes for coronary artery disease (CAD) and its different severities of atherosclerotic lesions. This study also aims to identify the change in expression patterns of DEGs in atherosclerotic lesions of single-vessel disease (SVD) and triple-vessel disease (TVD). The weight of different severities of lesion was estimated using a modified Gensini score. The gene expression profiling was performed using the Affymetrix microarray platform. The functional annotation for CAD was performed using DAVID v6.8. The biological network gene ontology tool (BiNGO) and ClueGO were used to explore the biological processes of functionally annotated genes of CAD. The changes in gene expression from SVD to TVD were determined by evaluating the fold change. Functionally annotated genes were found in an unique set and could be distinguishing two distinct severities of CAD. The biological processes such as cellular migration, locomotion, cell adhesion, cytokine production, positive regulation of cell death etc. enriched the functionally annotated genes in SVD, whereas, wound healing, negative regulation of cell death, blood coagulation, angiogenesis and fibrinolysis were enriched significantly in TVD patients. The genes THBS1 and CAPN10 were functionally annotated for CAD in both SVD and TVD. The 61 DEGs were identified, those have changes their expression with different severities of atherosclerotic lesions, in which 13 genes had more than two-fold change in expression between SVD and TVD. The consistent findings were obtained on validation of microarray gene expression of selected 10 genes in a separate cohort using real-time PCR. This study identified putative candidate genes and their biological processes predisposing toward and affecting the severity of CAD.
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Affiliation(s)
- Shiridhar Kashyap
- Department of Cardiology, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow 226 014, India.
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Liu L, Liu J, Wang H, Zhao H, Du Y. Fenretinide targeting of human colon cancer sphere cells through cell cycle regulation and stress-responsive activities. Oncol Lett 2018; 16:5339-5348. [PMID: 30250604 DOI: 10.3892/ol.2018.9296] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2015] [Accepted: 03/10/2017] [Indexed: 01/16/2023] Open
Abstract
Cancer stem cells (CSCs) are considered to be the main cause of chemoresistance and the resultant low survival rate of patients with cancer. N-(4-hydroxyphenyl)-retinamide, known as fenretinide or 4HPR, is a synthetic derivative of all-trans-retinoic acid. It is a promising anticancer agent, has minimal side effects and synergizes with other anticancer agents to reinforce their anticancer efficacy. The present study investigated whether fenretinide eliminated colon sphere cells. HT29 and HCT116 cells incubated in low-serum culture medium were more sensitive to fenretinide treatment than those incubated in full-serum medium. Colon spheres formed in serum-free medium demonstrated stem-like characteristics. The percentage of cluster of differentiation (CD) 44+ cells was significantly higher in sphere cells compared with parental cells. Sphere cells also demonstrated increased tumorigenic ability in non-obese diabetic/severe combined immunodeficiency mice. Fenretinide inhibited the formation of colon spheres in HT29 and HCT116 cells. Microarray, cell cycle and reverse transcription-quantitative polymerase chain reaction analysis revealed that fenretinide induced genes associated with cell cycle regulation and the stress response in fenretinide-treated HT29 sphere cells. To the best of our knowledge, the present study was the first to investigate the effect of fenretinide on colon stem cells. Fenretinide was demonstrated to preferentially target colon sphere cells, which may possess certain stem-like characteristics. These results are an important addition to the current knowledge concerning fenretinide, and provide a foundation for its clinical application in the treatment of cancer.
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Affiliation(s)
- Lanlan Liu
- Institute of Health Sciences, Shanghai Jiao Tong University School of Medicine (SJTUSM)/Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200025, P.R. China.,Graduate School of The Chinese Academy of Sciences, Shanghai 200031, P.R. China
| | - Jiansheng Liu
- Center for Reproductive Medicine, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200135, P.R. China.,Shanghai Key Laboratory for Assisted Reproduction and Reproductive Genetics, Shanghai 200135, P.R. China
| | - Haiwei Wang
- Institute of Health Sciences, Shanghai Jiao Tong University School of Medicine (SJTUSM)/Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200025, P.R. China
| | - Hui Zhao
- School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong 999077, SAR, P.R. China
| | - Yanzhi Du
- Center for Reproductive Medicine, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200135, P.R. China.,Shanghai Key Laboratory for Assisted Reproduction and Reproductive Genetics, Shanghai 200135, P.R. China
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Lai Y, Zhang F, Nayak TK, Modarres R, Lee NH, McCaffrey TA. An efficient concordant integrative analysis of multiple large-scale two-sample expression data sets. Bioinformatics 2018; 33:3852-3860. [PMID: 28174897 DOI: 10.1093/bioinformatics/btx061] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2016] [Accepted: 01/31/2017] [Indexed: 11/13/2022] Open
Abstract
Motivation We have proposed a mixture model based approach to the concordant integrative analysis of multiple large-scale two-sample expression datasets. Since the mixture model is based on the transformed differential expression test P-values (z-scores), it is generally applicable to the expression data generated by either microarray or RNA-seq platforms. The mixture model is simple with three normal distribution components for each dataset to represent down-regulation, up-regulation and no differential expression. However, when the number of datasets increases, the model parameter space increases exponentially due to the component combination from different datasets. Results In this study, motivated by the well-known generalized estimating equations (GEEs) for longitudinal data analysis, we focus on the concordant components and assume that the proportions of non-concordant components follow a special structure. We discuss the exchangeable, multiset coefficient and autoregressive structures for model reduction, and their related expectation-maximization (EM) algorithms. Then, the parameter space is linear with the number of datasets. In our previous study, we have applied the general mixture model to three microarray datasets for lung cancer studies. We show that more gene sets (or pathways) can be detected by the reduced mixture model with the exchangeable structure. Furthermore, we show that more genes can also be detected by the reduced model. The Cancer Genome Atlas (TCGA) data have been increasingly collected. The advantage of incorporating the concordance feature has also been clearly demonstrated based on TCGA RNA sequencing data for studying two closely related types of cancer. Availability and Implementation Additional results are included in a supplemental file. Computer program R-functions are freely available at http://home.gwu.edu/∼ylai/research/Concordance. Contact ylai@gwu.edu. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yinglei Lai
- Department of Statistics, The George Washington University, Washington, DC 20052, USA
| | - Fanni Zhang
- Department of Statistics, The George Washington University, Washington, DC 20052, USA
| | - Tapan K Nayak
- Department of Statistics, The George Washington University, Washington, DC 20052, USA
| | - Reza Modarres
- Department of Statistics, The George Washington University, Washington, DC 20052, USA
| | | | - Timothy A McCaffrey
- Division of Genomic Medicine, Department of Medicine, The George Washington University Medical Center, Washington, DC 20037, USA
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Hou J, Liu H, Wang L, Duan L, Li S, Wang X. Molecular Toxicity of Metal Oxide Nanoparticles in Danio rerio. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2018; 52:7996-8004. [PMID: 29944347 DOI: 10.1021/acs.est.8b01464] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Metal oxide nanoparticles can exert adverse effects on humans and aquatic organisms; however, their toxic mechanisms are still unclear. We investigated the toxic effects and mechanisms of copper oxide, zinc oxide, and nickel oxide nanoparticles in Danio rerio using microarray analysis and the comet assay. Copper oxide nanoparticles were more lethal than the other metal oxide nanoparticles. Gene ontology analysis of genes that were differentially expressed following exposure to all three metal oxide nanoparticles showed that the nanoparticles mainly affected nucleic acid metabolism in the nucleus via alterations in nucleic acid binding. KEGG analysis classified the differentially expressed genes to the genotoxicity-related pathways "cell cycle", "Fanconi anemia", "DNA replication", and "homologous recombination". The toxicity of metal oxide nanoparticles may be related to impairments in DNA synthesis and repair, as well as to increased production of reactive oxygen species.
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Affiliation(s)
- Jing Hou
- College of Environmental Science and Engineering , North China Electric Power University , Beijing 102206 , China
| | - Haiqiang Liu
- College of Environmental Science and Engineering , North China Electric Power University , Beijing 102206 , China
| | - Luyao Wang
- College of Environmental Science and Engineering , North China Electric Power University , Beijing 102206 , China
| | - Linshuai Duan
- College of Environmental Science and Engineering , North China Electric Power University , Beijing 102206 , China
| | - Shiguo Li
- Research Center for Eco-Environmental Sciences , Chinese Academy of Science , Beijing 100085 , China
| | - Xiangke Wang
- College of Environmental Science and Engineering , North China Electric Power University , Beijing 102206 , China
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Abstract
Throughout the past nearly a decade, the application of high-throughput sequencing to RNA molecules in the form of RNA sequencing (RNA-seq) and its many variations has revolutionized transcriptomic studies by enabling researchers to take a simultaneously deep and truly global look into the transcriptome. However, there is still considerable scope for improvement on RNA-seq data in its current form, primarily because of the short-read nature of the dominant sequencing technologies, which prevents the completely reliable reconstruction and quantification of full-length transcripts, and the sequencing library building protocols used, which introduce various distortions in the final data sets. The ideal approach toward resolving these remaining issues would involve the direct amplification-free sequencing of full-length RNA molecules. This has recently become practical with the advent of nanopore sequencing, which raises the possibility of yet another revolution in transcriptomics. I discuss the design considerations to be taken into account, the technical challenges that need to be addressed and the biological questions these advances can be expected to resolve.
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Inde T, Nishizawa S, Hattori Y, Kanamori T, Yuasa H, Seio K, Sekine M, Ohkubo A. Synthesis of and triplex formation in oligonucleotides containing 2'-deoxy-6-thioxanthosine. Bioorg Med Chem 2018; 26:3785-3790. [PMID: 29914771 DOI: 10.1016/j.bmc.2018.06.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2018] [Revised: 05/30/2018] [Accepted: 06/04/2018] [Indexed: 12/18/2022]
Abstract
This study aimed to synthesize triplex-forming oligonucleotides (TFOs) containing 2'-deoxy-6-thioxanthosine (s6X) and 2'-deoxy-6-thioguanosine (s6Gs) residues and examined their triplex-forming ability. Consecutive arrangement of s6X and s6Gs residues increased the triplex-forming ability of the oligonucleotides more than 50 times, compared with the unmodified TFOs. Moreover, the stability of triplex containing a mismatched pair was much lower than that of the full-matched triplex, though s6X could form a s6X-GC mismatched pair via tautomerization of s6X. The present results reveal excellent properties of modified TFOs containing s6Xs and s6Gs residues, which may be harnessed in gene therapy and DNA nanotechnology.
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Affiliation(s)
- Takeshi Inde
- Department of Life Science and Technology, Tokyo Institute of Technology, 4259 Nagatsuta, Midoriku, Yokohama 226-8501, Japan
| | - Shuhei Nishizawa
- Department of Life Science and Technology, Tokyo Institute of Technology, 4259 Nagatsuta, Midoriku, Yokohama 226-8501, Japan
| | - Yuusaku Hattori
- Department of Life Science and Technology, Tokyo Institute of Technology, 4259 Nagatsuta, Midoriku, Yokohama 226-8501, Japan
| | - Takashi Kanamori
- Department of Life Science and Technology, Tokyo Institute of Technology, 4259 Nagatsuta, Midoriku, Yokohama 226-8501, Japan
| | - Hideya Yuasa
- Department of Life Science and Technology, Tokyo Institute of Technology, 4259 Nagatsuta, Midoriku, Yokohama 226-8501, Japan
| | - Kohji Seio
- Department of Life Science and Technology, Tokyo Institute of Technology, 4259 Nagatsuta, Midoriku, Yokohama 226-8501, Japan
| | - Mitsuo Sekine
- Department of Life Science and Technology, Tokyo Institute of Technology, 4259 Nagatsuta, Midoriku, Yokohama 226-8501, Japan
| | - Akihiro Ohkubo
- Department of Life Science and Technology, Tokyo Institute of Technology, 4259 Nagatsuta, Midoriku, Yokohama 226-8501, Japan.
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Li J, Eberwine J. The successes and future prospects of the linear antisense RNA amplification methodology. Nat Protoc 2018; 13:811-818. [PMID: 29599441 PMCID: PMC7086549 DOI: 10.1038/nprot.2018.011] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2017] [Accepted: 01/04/2018] [Indexed: 12/03/2022]
Abstract
This Perspective discusses the development of the linear amplified RNA amplification technique over the last 25 years, and future applications of this important and versatile methodology. It has been over a quarter of a century since the introduction of the linear RNA amplification methodology known as antisense RNA (aRNA) amplification. Whereas most molecular biology techniques are rapidly replaced owing to the fast-moving nature of development in the field, the aRNA procedure has become a base that can be built upon through varied uses of the technology. The technique was originally developed to assess RNA populations from small amounts of starting material, including single cells, but over time its use has evolved to include the detection of various cellular entities such as proteins, RNA-binding-protein-associated cargoes, and genomic DNA. In this Perspective we detail the linear aRNA amplification procedure and its use in assessing various components of a cell's chemical phenotype. This procedure is particularly useful in efforts to multiplex the simultaneous detection of various cellular processes. These efforts are necessary to identify the quantitative chemical phenotype of cells that underlies cellular function.
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Affiliation(s)
- Jifen Li
- University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - James Eberwine
- University of Pennsylvania, Philadelphia, Pennsylvania, USA
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50
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Abstract
High-throughput biological technologies are routinely used to generate gene expression profiling or cytogenetics data. To achieve high performance, methods available in the literature become more specialized and often require high computational resources. Here, we propose a new versatile method based on the data-ordering rank values. We use linear algebra, the Perron-Frobenius theorem and also extend a method presented earlier for searching differentially expressed genes for the detection of recurrent copy number aberration. A result derived from the proposed method is a one-sample Student's t-test based on rank values. The proposed method is to our knowledge the only that applies to gene expression profiling and to cytogenetics data sets. This new method is fast, deterministic, and requires a low computational load. Probabilities are associated with genes to allow a statistically significant subset selection in the data set. Stability scores are also introduced as quality parameters. The performance and comparative analyses were carried out using real data sets. The proposed method can be accessed through an R package available from the CRAN (Comprehensive R Archive Network) website: https://cran.r-project.org/web/packages/fcros .
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Affiliation(s)
- Doulaye Dembélé
- Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), CNRS UMR 7104, INSERM U 1258, Université de Strasbourg, Illkirch-Graffenstaden, France
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